Estimation of network quality metrics from network request data

ABSTRACT

Network request data is collected over a time window. The network request data is filtered to generate bypass network traffic records. Network performance categories are generated from the bypass network traffic records. Sufficient statistics of network optimization parameters are calculated for the network performance categories. The sufficient statistics of the network optimization parameters are used to generate network optimization parameters to determine data download performances of web applications.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.16/775,819 filed Jan. 29, 2020, the contents of which are incorporatedherein by reference in their entireties. The applicant(s) hereby rescindany disclaimer of claim scope in the parent application(s) or theprosecution history thereof and advise the USPTO that the claims in thisapplication may be broader than any claim in the parent application(s).

This application is related to U.S. patent application Ser. No.16/775,807 (Attorney Docket No. 4476US; 80011-0061), titled “MACHINELEARNING BASED END TO END SYSTEM FOR TCP OPTIMIZATION,” by TejaswiniGanapathi, Satish Raghunath and Shauli Gal, filed on Jan. 29, 2020, thecontents of all of which are incorporated herein by reference in theirentireties.

TECHNOLOGY

The present invention relates generally to optimizing content delivery,and in particular, to estimation of network quality metrics from networkrequest data.

BACKGROUND

Access service networks are very volatile and diverse. Link conditionsmay vary in different access service networks and different locations.Metrics such as latency, jitter, throughput, and losses are hard tobound or predict. The diversity comes from the various networktechnologies, domain name services, plethora of devices, platforms, andoperating systems in use at the various locations of the access servicenetworks.

Transmission Control Protocol (TCP) plays an important role in thecontent delivery business: it provides a reliable, ordered, anderror-checked delivery of a stream of octets between applicationsrunning on hosts communicating by an IP network. Major Internetapplications, such as the World Wide Web, email, remote administration,and file transfer, rely on TCP. Numerous parameters may be used in TCPto help in ordered data transfer, retransmission of lost packets,error-free data transfer, flow control, and congestion control. However,identifying an optimal data value for a single TCP parameter based onchanging network characteristics remains a challenge.

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection. Similarly, issues identified with respect to one or moreapproaches should not assume to have been recognized in any prior art onthe basis of this section, unless otherwise indicated.

BRIEF DESCRIPTION OF DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings and in whichlike reference numerals refer to similar elements and in which:

FIG. 1A illustrates a high-level block diagram for a network performanceoptimization system; FIG. 1B illustrates example discovery,identification and/or generation of network quality categories fromnetwork traffic data through unsupervised machine learning;

FIG. 2 illustrates an example system that includes a network optimizer;

FIG. 3A illustrates an example process flow for estimating networkquality categories;

FIG. 3B illustrates an example process flow for driving a networkoptimization system with network quality mappings; FIG. 3C illustratesan example network optimization policy;

FIG. 4A through FIG. 4D illustrate example process flows; and

FIG. 5 illustrates an example hardware platform on which a computer or acomputing device as described herein may be implemented.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Example embodiments, which relate to estimation of network qualitymetrics from network request data, are described herein. In thefollowing description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. In other instances, well-known structures and devices are notdescribed in exhaustive detail, in order to avoid unnecessarilyoccluding, obscuring, or obfuscating the present invention.

Example embodiments are described herein according to the followingoutline:

-   -   1. GENERAL OVERVIEW    -   2. NETWORK TRAFFIC DATA AND PERFORMANCE OPTIMIZATION    -   3. NETWORK QUALITY CATEGORIES    -   4. SUFFICIENT STATISTICS    -   5. NETWORK QUALITY MONITORING AND OPTIMIZATION    -   6. SYNTHETIC TRAFFIC DATA GENERATION    -   7. EXAMPLE NETWORK OPTIMIZER    -   8. ESTIMATING NETWORK QUALITY CATEGORIES    -   9. DRIVING NETWORK OPTIMIZATION    -   10. EXAMPLE EMBODIMENTS    -   11. IMPLEMENTATION MECHANISMS—HARDWARE OVERVIEW    -   12. EQUIVALENTS, EXTENSIONS, ALTERNATIVES AND MISCELLANEOUS

1. General Overview

This overview presents a basic description of some aspects of anembodiment of the present invention. It should be noted that thisoverview is not an extensive or exhaustive summary of aspects of theembodiment. Moreover, it should be noted that this overview is notintended to be understood as identifying any particularly significantaspects or elements of the embodiment, nor as delineating any scope ofthe embodiment in particular, nor the invention in general. Thisoverview merely presents some concepts that relate to the exampleembodiment in a condensed and simplified format, and should beunderstood as merely a conceptual prelude to a more detailed descriptionof example embodiments that follows below.

Modern data transport networks feature a huge variety of networktechnologies, end-user devices, and software. Some of the common networktechnologies include wired networks, cellular networks (e.g., LTE, HSPA,3G, older technologies, etc.), WiFi (e.g., 802.11xx series of standards,etc.), satellite, microwave, etc. In terms of devices and software,there are smartphones, tablets, personal computers, network-connectedappliances, electronics, etc., that rely on a range of embedded softwaresystems such as Windows, Apple iOS, Google Android, Linux, and severalother specialized operating systems.

Many of these network technologies feature a volatile wireless lastmile. The volatility manifests itself in the application layer in theform of variable bandwidth, latency, jitter, loss rates and othernetwork related impairments. The diversity in devices, operating systemsoftware and form factors result in a unique challenge from theperspective of user experience. The nature of content that is generatedand consumed on these devices is quite diverse. The new content is verydynamic and personalized.

A consequence of these characteristics is that end-users andapplications experience inconsistent and poor performance. Client andserver software systems may be best deployed in a stable operatingenvironment where operational parameters either change a little or donot change at all. When such software systems see unusual networkfeedback or interaction, they tend to over-react in terms of remedies.

To maximize improvement in throughput gain and download complete time,network or TCP parameters may be estimated using a data driven approachby analyzing prior network traffic data without assuming anystationarity of network performance/quality and/o without assuming anystationarity of probability distributions for each of the quantitiesused in network optimization as estimated/predicted/determined by anetwork optimizer as described herein. Because computer networks may bevolatile and non-stationary (i.e., statistics change with time),estimating network or TCP parameters as described herein can be adaptiveand/or dynamically performed to capture volatilities in the networks,but also stable and not overly sensitive to short term fluctuations.

In many operational scenarios, prior network traffic data collected formonitoring and optimizing network performance may not containinformation about access service network technology used by user devicesand locations of the user devices. For example, application servers forweb applications may receive web messages such as HTTP messages withstandard headers devoid of access service network technology informationfrom user devices accessing the web applications from web browsers. Inaddition, application servers for web applications may interact withuser devices such as desktop computers and other non-cellular devicesthat are not able to provide location information about where networkrequests are originated. A network request as described herein may referto a data request for retrieving or downloading one or more data objectsfrom one or more servers located remotely across one or more networks.Example network requests may include, but are not necessarily limited toonly, any of: HTTP requests, RESTful API calls, XML HTTP requests,application programming interface (API) calls, etc.

As used herein, a web application may refer to a cloud-based computerapplication that is hosted in one or more cloud-based networks of aprovider such as a multi-tenant hosting system that are different fromaccess service networks directly interfacing with user devices. In manyoperational scenarios, the access service networks (e.g., WiFi,cellular, DSP, optical, copper wired, access service satellitecommunications, etc.) and the one or more cloud-based networks of theprovider of the web application may be interconnected through zero, oneor more of core networks, direct network conduits, and so forth. The webapplication can be accessed and used by the user devices by way of theaccess service networks and one or more application services located at(e.g., edges of, endpoints of web servers in, etc.) the one or morecloud-based networks of the provider. In some operational scenarios, theprovider of the web application may deploy multiple web applicationssuch as sales, marketing, reporting, training, etc., that can besimilarly accessed and used by user devices by way of applicationservers operating in conjunction with backend servers and databasesdeployed with the one or more cloud-based networks.

Techniques as described herein can be used to provide a networkoptimization solution, including but not limited to an end-to-endmachine learning solution, to optimize network or TCP parameters such ascongestion control, concurrency parameters, and the like, to maximizeimprovements in download outcomes and/or page loading performances inconnection with user devices accessing web applications. Thesetechniques may make use of, but do not depend on, (1)information/metadata on access (service) network technology used inaccess (service) networks and (2) location context/information of theuser devices and/or the access service networks.

An access service network (or access network for simplicity) may referto any network that directly interface with a user device as describedherein or with a network device such as router, modem, etc., collocatedand connected with the user device. Example access service networks mayinclude, but are not necessarily limited to only, any, some or all of:WiFi networks, cellular network, wired access networks, satelliteenabled access networks, networks having their service areas physicallycovering user devices and/or network devices collocated and connectedwith the user devices, networks providing (e.g., initial, etc.) networkaccess to user devices and/or network devices collocated and connectedwith the user devices, etc.

The techniques as described herein can be beneficially used orimplemented with a wide variety of network (performance) optimizersimplementing a wide variety of network optimization methods to optimizeend user experience through faster page loads and download outcomes inconnection with web pages provided on web browsers, whether the webbrowsers run on mobile devices, desktops, or other types of computingdevices. These techniques are agnostic to types of network optimizationsystems and can work with any such systems to add or enrich collectedtraffic data information with network quality information for thepurpose of evaluating, monitoring and/or improving network quality.

In some operational scenarios, even with access to information carriedin data packets or HTTP messages, a network optimizer may not be able toaccess or gather contextual information for the access service networks(e.g., 3/4/5G or WiFi technologies, specific carriers providing theaccess service networks, etc.), geographic and time zones and/orlocations of the user devices or the access service networks, etc.

Under techniques as described herein, a network optimizer may beimplemented as a data driven system that can improve network experienceof end users with no or minimal contextual information (e.g., about theaccess service networks, the time zones of the user devices and/or theaccess service networks, the locations of the user devices and/or theaccess service networks, etc.). The system can be implemented toadaptively respond to changes in network quality and congestion overtime and estimate optimized values for network or TCP parameters to beused by user devices and/or servers to improve page load performance anddownload outcomes over what is currently achieved with default networkor TCP parameters without optimization.

Various modifications to the preferred embodiments and the genericprinciples and features described herein will be readily apparent tothose skilled in the art. Thus, the disclosure is not intended to belimited to the embodiments shown, but is to be accorded the widest scopeconsistent with the principles and features described herein.

2. Network Traffic Data and Performance Optimization

The network performance of data delivery for user devices to sendnetwork requests to web applications and receive data downloadscorresponding to the network requests is closely tied to networkconditions with which the user devices, application servers and otherinvolved devices are operating. A network optimizer as described hereindynamically adapts to these conditions and picks the best networkoptimization policies/strategies based on network performance datasets(e.g., page load performance data, download outcomes, sufficientstatistics of parameters related to network performance, performancequality mappings, training instances, etc.) generated from networktraffic data collected over a series of time windows for webapplications that communicates with user devices and/or network requestsimulators from different access (service) networks by way ofapplication servers located at different geographic and time zonelocations.

Techniques as described herein can be implemented without reliance oncollecting or capturing some or all information about specific contextsin which network requests and/or associated data downloads are made. Asused herein, network requests generated by network request originationdevices such as user devices and/or network request simulators may referto any of: network download requests, data transfer requests, requeststo download data, requests to download data objects, request to load webpages, etc.

The network optimizer may be implemented to recommend, but is notlimited to, any combination of: end-device based data deliverystrategies and accelerator-based data delivery strategies. The networkoptimizer may perform some or all of its operations without (capturingor receiving) information about some or all specific operating contextsin which some or all of the network requests giving rise to the networkperformance datasets were made. However, if information about some orall specific operating contexts in which some or all of the networkrequests giving rise to network performance datasets were made isavailable, the network optimizer may operate to make use or takeadvantage of such information.

Network optimization policies or strategies refer to methods and/oroperational parameters (e.g., network or TCP parameters, etc.) deployedor implemented by a network request origination device such as a userdevice and/or a network request simulator to request, receive or,transmit data or data object(s) over the network. These networkoptimization policies or strategies include, but are not limited to, anycombination of: optimized values of one or more network or TCPparameters to propagate to network request origination devices and/ornetwork request processing devices for implementation on the networkrequest origination devices and/or the network request processingdevices to be used in future network requests and future data downloadscorresponding to the future network requests.

A range of network or TCP parameters determines and influences theperformance of tasks such as data delivery in connection with networkrequests and/or data downloads. With volatility and diversity, there isan explosion in the number of parameters that may be significant. Byisolating parameters, significant acceleration of data delivery may beachieved. Networks, devices and content are constantly changing. Variousmethods of optimizing data delivery are described in U.S. PatentPublication No. 2014/0304395, entitled “Cognitive Data DeliveryOptimizing System,” filed Nov. 12, 2013, and which is herebyincorporated by reference in its entirety for all purposes. Embodimentsare not tied down by assumptions on the current nature of the system. Anetwork optimizer 106 may use raw network traffic data to generate anetwork performance dataset and use the network performance dataset togenerate network optimization policies or strategies as describedherein.

FIG. 1A and the other figures use like reference numerals to identifylike elements. A letter after a reference numeral, such as “102a,”indicates that the text refers specifically to the element having thatparticular reference numeral. A reference numeral in the text without afollowing letter, such as “102,” refers to any or all of the elements inthe figures bearing that reference numeral (e.g. “102” in the textrefers to reference numerals “102a,” and/or “102b” in the figures). Onlyone user device 102 (end-devices as described above) is shown in FIG. 1Ain order to simplify and clarify the description.

As illustrated in FIG. 1A, a system 100 includes a user device 102 thatcommunicates data requests through a network 104. In some operationalscenarios, system 100 includes a network request simulator 118 thatcommunicates simulated data requests through network 104. An applicationserver 108 may receive the data requests—as originated from user devicesand/or network request simulators over network 104—and communicate therequests to a data center 110.

Example application servers may include but are not necessarily limitedto only, any of: web servers with service end points and service logicto further invoke backend application, database and/or platformservices, application servers operating in conjunction with web serversand backend servers, application servers handling bypass traffic and/oraccelerated traffic, proxy servers acting as proxies for some or all ofapplication servers and/or backend servers, and so forth. The networkoptimizer 106 may gather network traffic data from the applicationserver 108 and store the network traffic data in the network datatraffic store 112, in an embodiment.

Example network traffic data may include, but is not necessarily limitedto only, network traffic data of data values used with network requestsmade by network request origination devices, page load data orperformance measurements of web/application page loadings on the networkrequest origination devices, download outcomes or performance data ofdata (object) downloads initiated by the network requests, etc.

Network traffic may refer to network requests sent by user devicesand/or network request simulators to application servers and/or data(object) downloads, as requested by the network requests, from theapplication servers to the user devices and/or the network requestsimulators.

To track or validate results of network optimizationpolicies/strategies, network requests may be divided and flagged intotwo network request types. The first network request type is acceleratednetwork requests (and their corresponding data downloads) optimized withat least one network optimization policy/strategy produced by a networkoptimization system as described herein. The second network request typeis bypass network requests (and their corresponding data downloads)transmitted directly between network request origination serversaccessing computer applications and application servers (or originservers) implementing the computer applications. As used herein, anorigin server may represent a server that (1) implements a web-basedcomputer application/service and/or provides/generates original data ordata objects requested by user devices and/or (2) provides originalcontent that may be cached and served out on behalf of the origin serverto user devices by a content delivery network (CDN).

Thus, a subset of network traffic may be bypass network trafficperformed with default TCP parameters; corresponding network requestdata including but not limited to performance data of the subset ofnetwork traffic may be stored in the network data traffic store 112 asbypass traffic data. In some operational scenarios, the bypass networktraffic may refer to network traffic generated by network requestsand/or data downloads transmitted directly between origin servers anduser devices without network optimization and/or without going throughnetwork accelerators implementing network optimizationpolicies/strategies as described herein.

Accelerated network traffic may refer to accelerated network requestsand/or accelerated data (object) downloads, which are performed withassigned optimized values of network or TCP parameters generated orpropagated from a network optimizer or a network optimization engine asdescribed herein. Corresponding network traffic data of the acceleratednetwork traffic may be collected, processed and/or stored in the networkdata traffic store 112 as accelerated traffic data.

After or concurrently while a page load or data download (e.g., withrespect to each data object of a web page, with respect to a group ofdata objects, etc.) is processed and/or completed, statistics of datasizes, times, etc., may be logged as raw network traffic data comprisingdatabase records representing raw traffic data records. Each raw trafficdata record may include performance metrics derived from or recorded inthe statistics. In an example, page load (performance) data such as someor all of page load throughputs, page load complete times, and page loadtimes to first byte, etc., may be captured in each database recordrepresenting a raw traffic data record in the network data traffic store112 for each network request and/or for a web page corresponding to thenetwork request. In another example, download outcomes such as some orall of download throughputs, download complete times, and download timesto first byte, etc., may be captured in each database recordrepresenting a raw traffic data record in the network data traffic store112 for each network request or data downloads corresponding to eachsuch network request.

Additionally, optionally or alternatively, performance metrics such aspercentage improvement in throughput and download complete time ofaccelerated network requests with optimized values of network or TCPparameters as compared with those of bypass traffic network requests mayalso be stored in raw traffic data records and/or subsequentprocessed/aggregated traffic data records in the network data trafficstore 112, in one embodiment.

In some operational scenarios, additional information may also beincluded in each database record or network traffic data record, inother embodiments. Typical sources of data relating to the networkenvironment are elements in the network infrastructure that gatherstatistics about transit traffic as well as user devices and/or networkrequest simulators that connect to the network as clients or servers.The additional information includes, but is not limited to, anycombination of: data pertaining to requests for objects, periodicmonitoring of network elements (which may include inputs from externalsource(s) as well as results from active probing), exceptional events(e.g., unpredictable, rare occurrences, etc.), data pertaining to thedevices originating or servicing requests, data pertaining to theapplications associated with the requests, data associated with thenetworking stack on any of the devices/elements that are in the path ofthe request or available from any external source, etc.

In an embodiment, the network request simulator 118 may be installed ona (e.g., a dedicated, a multi-purposed, etc.) test computing device,which may be separate from a user device—e.g., actually operated by auser of a computer application as described herein—such as 102 of FIG.1A.

In an embodiment, a module/component such as an agent 114 may beinstalled in a network request origination device such as the userdevice 102 and/or the network request simulator 118 to provide inputsabout the real-time operating conditions, participates and performsactive network measurements, and executes recommended strategies withactual or synthetic network requests. The agent 114 may be supplied in asoftware development kit (SDK) and installed on the user device 102and/or the network request simulator 118 when an application thatincludes the SDK is installed on the user device 102.

Additionally, optionally or alternatively, in an embodiment, the networkrequest simulator 118 may be supplied in the software development kit(SDK) and installed on the user device 102. For example, in someoperational scenarios, the network request simulator 118 may becollocated with the user device 102 as a part of the agent 114.

By incorporating an agent 114 in or with the network request originationdevice to report the observed networking conditions back to theaccelerator 116, estimates about the state of the network can be vastlyimproved. The main benefits of having a presence (the agent 114) in orwith the network request origination device such as the user device 102and/or the network request simulator 118 include the ability to performmeasurements that characterize one leg of the session, e.g., measuringjust the client-to-server leg latency, etc.

An accelerator 116 sits in the path of the data traffic operating withan application server 108 and executes recommended strategies inaddition to gathering and measuring network-related information inreal-time. The accelerator 116 may propagate network optimizationpolicies (which, unless specified otherwise, may interchangeably referto network optimization strategies herein) from the network optimizer106 to the application server 108, in one embodiment. In anotherembodiment, the agent 114 may implement one or more network optimizationpolicies from the network optimizer 106.

For example, the optimal number of simultaneous network connections maybe propagated as a network optimization policy from the networkoptimizer 106 through the network 104 to the agent 114 embedded on orwith the user device 102 and/or the network request simulator 118. Asanother example, the transmission rate of file transfer may be limitedto 20 MB/sec by the accelerator 116 as a network optimization policypropagated by the network optimizer 106 based on machine learning (e.g.,unsupervised learning, supervised learning, etc.) and performancemetrics.

Here, the term “supervised learning” may refer to providing to andtraining a learning machine (or machine learner) with, datasetscomprising labels or ground truth indicating desired outputs as labeled.An example dataset used by supervised learning as described herein is anetwork performance dataset having network quality mappings comprisingrespective labels or ground truth (e.g., estimated/determined byunsupervised learning, etc.) indicating respective traffic shares in thenetwork quality mappings as “good”, “ok” or “bad” network qualitycategories.

In contrast, the term “unsupervised learning” may refer to learningstructures/features from, datasets comprising no labels or ground truthindicating desired outputs. An example of unsupervised learning may be,but is not necessarily limited to only: automatically clustering data inthe datasets into classes, categories, bands of differentcharacteristics or features. An example dataset used by unsupervisedlearning as described herein is network traffic data (e.g., page loadperformance data, download outcomes, etc.) without some or allcontextual information about access service networks (e.g., a “good”network such as a 5G network, an “ok” network such as a 4G network, a“bad” network such as a 3G or 2G network, etc.) or geographic and/ortime zone locations (e.g., in a modern company office with good networkservice coverage, in an area with poor network coverage, etc.) ofnetwork request origination devices.

The raw network traffic data may be aggregated or processed into (e.g.,aggregated, processed, etc.) network traffic data comprising aggregatedrows or database records (or traffic data records). For example, once amultitude of raw network traffic data associated with data requestsbetween network request origination devices such as user devices 102and/or network request simulators 118 and the data centers 110 arelogged in the network data traffic store 112, it becomes possible tofilter, classify, cluster, partition, group and/or aggregate this databy unique combinations of some or all of network quality category. Forexample, the network quality categories may be generated withunsupervised learning or automatic clustering algorithms.

In some operational scenarios, aggregated rows such as database recordsor network traffic data records for a network quality category may bepartitioned into different data segments respectively corresponding todifferent combinations of computer applications (e.g., individualcomputer applications, individual computer application types, individualcomputer application names, individual application identifiers, etc.),application servers (e.g., individual application servers servingnetwork requests, etc.), time blocks (e.g., ordered time windows,consecutive time windows, fixed time windows, fixed 24-hours intervals,fixed two-hour intervals, non-fixed time windows, 1-hour time windows inbusy hours, 3-hour time windows in non-busy hours, etc.), and so forth.

Filtering, classifying, clustering, partitioning, grouping and/oraggregating of the raw network traffic data by time block/window asdescribed herein may record (e.g., individual, network request specific,collective, network request non-specific, moving average over a timeinterval, etc.) network request data, page load data, download outcomes,traffic shares, etc. In some operational scenarios, for acceleratedtraffic, each aggregated row representing an accelerated network trafficportion also records performance improvement metrics such as percentageimprovement in comparison with the bypass traffic in page loadperformance, data download performance, etc.

3. Network Quality Categories

Techniques as described herein can be implemented to dynamicallyidentify categories of network quality from network traffic data. Thecategories of network quality may be referred to, unless otherwisespecified, as network quality/performance categories, networkquality/performance bands, network quality/performance clusters, etc. Asused herein, network quality or network performance may refer to qualityor performance of one or more networks (e.g., 104 of FIG. 1A, etc.)through which user devices and/or network request simulators sendnetwork requests for download data objects and receive data (object)downloads as requested as measured through page load data, downloadoutcomes, etc., with dependence on receiving contextual informationabout underlying access service networks (or types thereof) whoseservice areas physically cover the user devices and about geographiclocations of the user devices and/or the access service networks.

Network request data may include, but are not necessarily limited toonly, some or all of: data fields used in network requests, operationalparameters carried in the network requests, performance data collectedfor the network requests, operational parameters used in user devicesand/or servers for the network requests, etc. Page load data mayinclude, but is not necessarily limited to only, some or all of: timestaken for individual web pages to fully load in browsers after links tothe web pages have been selected or clicked, time taken for individualdata objects of web pages to be downloaded, time taken for processingdownloaded data objects (including but not limited to scripts,browser-executable computing instructions, etc.) by browsers, etc.Download outcomes may include, but are not necessarily limited to only,some or all measurements of throughput, time to first byte, and downloadcomplete time for individual downloads, etc.

FIG. 1B illustrates example discovery, identification and/or generationof network quality categories 122 from network traffic data throughunsupervised machine learning 124 implemented with one or more computingdevices such as a network optimizer, a network optimization system, anetwork optimization engine, etc.

The network traffic data may comprise time series data represented as atime series of individual per-time-window network traffic data portions.For example, a total time period over which the network traffic data iscollected for adaptive network performance optimization may bepartitioned or accumulated into a series of consecutive (e.g., mutuallyexclusive, partly overlapped, etc.) time windows. Correspondingly, thenetwork traffic data may be partitioned or accumulated into a series ofconsecutive (e.g., mutually exclusive, partly overlapped, etc.)per-time-window network traffic data portions. Each per-time-windownetwork traffic data portion in the series of per-time-window networktraffic data portions corresponds to—e.g., may be indexed by and/or maybe collected/measured over—a corresponding time window in the series oftime windows.

The unsupervised machine learning 124 can be performed on eachper-time-window network traffic data portion (e.g., 120, etc.) in thetime series to dynamically identify network quality categories 122 asrepresented in the underlying per-time-window network traffic dataportion 120. The unsupervised machine learning 124 can be used toautomatically discover, identify and/or generate performance qualityclusters/categories in the network traffic data without receiving anylabels or ground truth of specific performance quality categories/typesto which any network requests and/or data downloads in the networktraffic data belong as input. Example unsupervised machine learningalgorithms/methods/procedures/operations employed in the unsupervisedmachine learning 124 may include, but is not necessarily limited toonly, some or all of: automatic cluster analysis, automatic principalcomponent analysis, density analysis, dimension analysis, k-means,hierarchical clustering, neural networks, autoencoders, generativeadversarial networks, etc.

One or more types of performance measurements/metrics collected in thenetwork traffic data may be used as features or to form feature vectorsfor applying the unsupervised machine learning 124. It should be notedthat a performance metric/measurement as described herein may benumeric, discrete, composite, and/or multi-dimensional.

For example, a corresponding per-time-window network traffic dataportion (e.g., 120, etc.) collected for each time window may comprise aset of database records (or a set of traffic data records) comprising aset of measurements of page load times of web pages corresponding to ortaken/measured for a set of network requests and/or data downloads forweb pages or for data objects therein. Each database record in the setof database records may comprise respective measurements of page loadtimes corresponding to or taken/measured for a respective networkrequest (and/or a respective data download) in the set of networkrequests. The set of page load times may be used as features or to formfeature vectors for automatic clustering of the databaserecords—representing underlying network requests and/or datadownloads—to discover, identify and/or generate different performancequality clusters/categories in the database records for the specifictime window.

In some operational scenarios, different performance qualityclusters/categories may be automatically discovered, identified and/orgenerated using a distance-like measure (e.g., a L2 distance measure, amagnitude measure, a sum of absolute differences, etc.) to minimizeintra-cluster distances between or among features or feature vectors ofdifferent network requests and/or corresponding downloads in the samecluster and to maximize inter-cluster distances between features ofnetwork requests and/or corresponding data downloads in a performancequality cluster/category and features or feature vectors of networkrequests and/or corresponding data downloads in a different performancequality cluster/category.

Additionally, optionally or alternatively, instead of directlyperforming unsupervised machine learning on a per-time-window networktraffic data portion (e.g., 120, etc.)—which comprises a set of databaserecords representing underlying network requests and/or correspondingdata downloads—as a whole, the per-time-window network traffic dataportion 120 may be partitioned into one or more data segments based onone or more internal and/or external attributes of database records inthe set of database records. For example, the set of database recordsfor the time window may be partitioned into different data segments orsubsets of database records based on computer application names (e.g.,computer application types, implemented by platform servers of amultitenant hosting system remote from user devices, etc.), geographiclocations of closest servers designated—or otherwise available—forserving or processing the underlying network requests represented in theset of database records, etc. Unsupervised machine learning (e.g., 124,etc.) as described herein may be performed individually on individualdata segments or individual subsets of database records to automaticallydiscover, identify and/or generate individual performance qualitycategories/clusters for each data segment or each subset of databaserecords.

4. Sufficient Statistics

Techniques as described herein can be further used or implemented togenerate sufficient statistics for network optimization parameters foridentified categories of network quality/performance, given networktraffic data as input.

As used herein, network optimization parameters may refer to some or alloperational parameters used in network optimizationoperations/algorithms/methods/procedures to generate optimized valuesfor network parameters to be implemented by user devices and/or byservers interacting with the user devices. Example network optimizationparameters may include, but are not necessarily limited to only, some orall of: sustained maximum transmit rate, maximum allowed burst, maximumlatency, etc., of each identified category of network quality orperformance. Example network parameters may include, but are notnecessarily limited to only, some or all of: TCP parameters, maximumburst sizes, initial congesting windows, TCP congestion handlingthresholds, etc.

FIG. 4A illustrates an example process flow for generating sufficientstatistics of network optimization parameters given input data 402comprising a network quality category and a time window. The performanceflow may be implemented or performed by a network monitor, a performanceoptimizer, a performance tester, etc., implemented with one or morecomputing devices. In some operational scenarios, a sufficientstatistics generator (e.g., 222 as illustrated in FIG. 2, etc.) mayperform this process flow.

Block 404 comprises querying network traffic data based on the networkquality category and the time window. Database records (or networktraffic data records) corresponding to (e.g., a combination of, etc.)the network quality category and the time window may be retrieved in aquery of the network traffic data store 112. The network optimizationparameters may individually and/or collectively define or inform qualityor performance of one or more networks (e.g., 104, etc.) for the networkquality category over the time window.

Block 406 comprises inferring sufficient statistics of probabilitydistributions for the network optimization parameters 408 from thedatabase records corresponding to (the combination of) the networkquality category and the time window. Sufficient statistics of aprobability distribution for each of the network optimization parametersfrom the database records of the network quality category and the timewindow using generative models for the network optimization parameters.

By way of illustration but not limitation, the network optimizationparameters or quantities may be some or all of: sustained maximumtransmit rate, maximum allowed burst, and maximum latency of the networkquality category and the time window as inferred or estimated fromnetwork request data, page load data, download outcomes, etc., asrepresented in the database records of the network quality category andthe time window retrieved in the query of the network traffic data store112.

Sufficient statistics as described herein may refer to a (e.g.,marginal, etc.) probability distribution or parameters (including butnot limited hyperparameters) characterizing, defining and/or specifyinga probability distribution. Sufficient statistics of a networkoptimization parameter can be used to provide sufficient informationabout a (e.g., marginal, etc.) probability distribution of the networkoptimization parameter such that expectation value(s) and otherstatistics of the network optimization parameter can be derived directlyfrom the sufficient statistics of the network optimization parameter.The sufficient statistics can be sampled or used to generate (e.g.,different, in certain value range(s) or certain value sub-range(s),etc.) values of network optimization parameter(s). Page load and/ordownload performances/outcomes of web applications with respect tospecific web pages or components/objects therein can then be checkedout, estimated, predicted and/or validated with these values of thenetwork optimization parameter(s). Because the sufficient statistics aregenerated based on generative models, this allows complementarysynthetic traffic data to be collected for monitoring, improving and/orvalidating (e.g., validating the generative models, validatingapplication improvement, validating corner/outlier cases/scenarios notnecessarily covered well by the generative models, etc.) variousperformances of the web applications with the sampled values from thesufficient statistics before releasing the web applications or newfeatures thereof to end users.

Example probability distributions as described herein may include, butare not necessarily limited to, any of: Gaussian probabilitydistributions, Poisson probability distributions, Erlang distributions,gamma distributions, neural network predicted probability distributions,noiseless probability distributions, noise-injected probabilitydistributions, etc. Some or all of the probability distributions may becharacterized by specific hyperfunction parameters associated withspecific functional forms to which the probability distributions belong.Once these hyperfunction parameters are derived or estimated for theprobability distributions, expectation values and other statistics(e.g., mean, median, quartiles, sampled network optimization parametervalues/ranges and respective traffic shares, etc.) may be derived orestimated using these hyperfunction parameters for the probabilitydistributions.

In some operational scenarios, given a functional form for a probabilitydistribution of a network optimization parameter, sufficient statisticsfor the probability distribution of the network optimization parametermay be represented by (derived or estimated values for) hyperfunctionparameters associated with the probability distribution or functionalform. Some example sufficient statistics may include, but are notnecessarily limited to only: some or all of: mean and varianceparameters for Gaussian distributions, lambda parameters for Poissondistributions, shape or rate parameters for Erlang distributions, and soforth.

As in the case of generation of performance quality categories throughunsupervised machine learning, sufficient statistics (of a probabilisticdistribution) of a network optimization parameter may be generatedthrough unsupervised machine learning as well. In some operationalscenarios, the sufficient statistics of the probabilistic distributionof the network optimization parameter can be inferred, estimated and/orgenerated through unsupervised machine learning using a generativemodel.

In some operational scenarios, a specific functional form type (e.g., aGaussian distribution, etc.) may be selected, from among a plurality ofcandidate functional form types (e.g., Gaussian distribution, Poissondistribution, etc.), to represent a probability distribution of anetwork optimization parameter for (a combination of) a network qualitycategory and a time window. The specific functional form type may beused as a generative model.

Sufficient statistics of the probability distribution of the networkoptimization parameter may be obtained by estimating, predicting andfitting hyperfunction parameters of the specific functional form type orthe generative model based on database records (or network traffic datarecords) for (the combination of) the network quality category and thetime window.

Additionally, optionally or alternatively, other generative models(e.g., Gaussian mixture model, hidden Markov models, naive Bayesautoregressive models, variational autoencoders, generative adversarialnetworks, etc.) other than a specific functional form used to representa probability distribution may be used. In some operational scenarios,neural networks may be used as generative models for inferring orestimating sufficient statistics and/or underlying probabilitydistributions of network optimization parameters for (a combination of)a network quality category and a time window.

5. Network Quality Monitoring and Optimization

In many operational scenarios, quality or performance of networks (e.g.,104 of FIG. 1A, etc.) depends on a variety of factors or contextsextrinsic to data values employed in network requests and/or datadownloads and performance data associated with the network requestsand/or data downloads. These factors or contexts may include, but arenot necessarily limited to only, some of all of the following:

-   -   mobile carrier;    -   device settings, such as phone/browser;    -   access network technology (which depends on network type such as        LTE, 3/4/5 G, HSPA, quality of WiFi, etc.)    -   autonomous systems to which user devices and/or application        servers belong;    -   real time network congestion handling and timeouts; etc.

Metadata about some or all of the factors and/or contexts affecting thequality or performance of the networks may or may not be available inmany operational scenarios.

Techniques as described herein do not depend on acquisition ofcontextual metadata or information about these factors including but notlimited to metadata about network technology or geographic locations (ornetwork topology locations). There is no inherent need to implement thetechniques as described herein in a way customized to specific networktechnologies, specific devices, specific CDNs, or specific content beingrequested or downloaded.

Rather, these techniques may be implemented in a manner that makes useof (e.g., solely, substantially, etc.) already available network trafficdata such as network request data, page load data, download outcomes,etc., that can be obtained in real time or as post download data,independent of or with no dependence on receiving information aboutunderlying access service networks (or types thereof) whose serviceareas physically cover the user devices and about geographic and/or timezone locations of the user devices and/or the access service networks.

The “goodness” of network quality or performance most likely affects enduser experiences of computer applications significantly. Just as theaforementioned factors or contexts in which network request are made aretime variant/dependent, quality and performance of networks are alsotime variant/dependent. Thus network traffic data can be evaluated overtime to capture and discern dynamic time variant nature of networkperformance and quality for the purpose of generating and applying bestnetwork optimization solutions adaptive to such dynamic time variantnature of network performance and quality, thereby improving end userexperiences of various computer applications.

In some operational scenarios, generation/determination of networkquality categories and sufficient statistics of probabilitydistributions of network optimization parameters for respective networkquality categories may be repeated (e.g., periodically, on demand, basedon a schedule, etc.) over time. By way of example but not limitation,unsupervised learning operations to generate network quality categoriesand sufficient statistics may be periodically repeated every $N$ hours,where $N$ is configurable in a system (e.g., a network optimizer, adynamic network performance monitor, etc.) as described herein.

Running unsupervised machine learning as described herein over time(e.g., periodically, etc.) or over a plurality of different time points(e.g., on a time schedule, etc.) to learn performance/quality relatedstructures of the network traffic data enables the system to dynamicallyidentify clusters, categories, segments, bands, etc., of various networkqualities from the network traffic data without any presumption orcontextual information about the performance and quality of accessservice networks involved and without any presumption and contextualinformation about whether the user devices are located in a good, ok orbad access networks. In addition, unsupervised machine learning can beenhanced and/or extended to further estimate sufficient statistics ofprobability distributions of network optimization parameters/quantitiesfor network quality clusters/categories/segments/bands dynamicallyidentified/determined from the network traffic data. Expected values orother statistics and/or their respective traffic shares can be readilyestimated or derived using the sufficient statistics of the networkoptimization parameters. The network quality categories and theirrespective sufficient statistics can be used—e.g., in place of, or inlieu of, the factors and contexts not available—to add granularity tonetwork traffic data as well as individual traffic data segments of thenetwork traffic data being analyzed for generating the networkoptimization solutions.

As a result, dynamic time-dependent estimation of network qualitycategories over multiple time windows can be deduced or inferred fromthe network traffic data without assuming any stationarity of networkquality categories or probability distributions for the networkoptimization parameters.

In some operational scenarios, network quality categories and/orsufficient statistics of probability distributions of networkoptimization parameters and/or expected values or other statistics alongwith their respective traffic shares, as generated through unsupervisedmachine learning as described herein, can be used for generating networkoptimization solutions/strategies/policies in scenarios in whichmetadata of factors and contexts such as network technology and locationcontext information is not available.

In some operational scenarios, network quality mappings can be generatedfrom individual data segments as partitioned or characterized byrespective sufficient statistics and/or expected values and/or trafficshares in network quality categories and used to train a networkoptimizer such as an adaptive network optimization engine to generatenetwork optimization policies for different network quality categoriesand/or for different data segments or different traffic shares ordifferent sub data segments in a network quality category.

For example, based at least in part on expected values or otherstatistics and their respective traffic shares generated with thesufficient statistics, the adaptive network optimization engine mayderive, estimate and/or predict optimized values of network or TCPparameters for individual data segments with the respective trafficshares. The optimized values of the network or TCP parameters may bepropagated as a part of network optimization policies to user devices,content distribution network (CDN) nodes/servers, web servers,application servers, platform servers, backend servers, etc., toaccelerate (future) network requests and/or (future) data downloads.

6. Synthetic Traffic Data Generation

FIG. 4B illustrates an example process flow for generating simulated orsynthetic network requests given input data 422. The performance flowmay be implemented or performed by a network monitor, a performanceoptimizer, a performance tester, etc., implemented with one or morecomputing devices.

By way of illustration but not limitation, the input data 422 comprisesa network quality category, a time window and a vector—corresponding tothe network quality category and the time window—of sufficientstatistics of one or more network optimization parameters. The vector ofsufficient statistics may comprise one or more vector componentsrespectively for the one or more network optimization parameters. Eachvector component of the vector corresponds to a specific networkoptimization parameter in the one or more network optimizationparameters. The one or more network optimization parameters may comprisesustained maximum transmit rate, maximum allowed burst and maximumlatency of the network quality category for the time window.

Block 424 comprises sampling a set of specific values for the one ormore network optimization parameters from a set of one or moreprobability distributions respectively for the one or more networkoptimization parameters. Each probability distribution in the set of oneor more probability distributions corresponds to a respective networkoptimization parameter in the one or more network optimizationparameters and is represented by a respective vector component (orrespective sufficient statistics) in the vector of sufficientstatistics. The set of specific values for the one or more networkoptimization parameters may correspond to a specific traffic share in aplurality of traffic shares that make up all traffic shares (or networkrequests and/or data downloads) for the network quality category and thetime window.

Block 426 comprises simulating network requests and/or data downloadswith the set of values for the set of network optimization parameters.For example, the set of values for the set of network optimizationparameters may be used to derive specific values for network or TCPparameters using a generative model, using a network optimizationfunction, and the like, that maps the network optimization parameters tocorresponding network or TCP parameters. A network request simulator(e.g., 118 of FIG. 1A, a Linux system with “curl” calls, tc, CatchPoint,etc.) may be used to generate or simulate the network requests or tocause corresponding data downloads to occur. Using statistics loggedwith these network requests and corresponding data downloads, syntheticnetwork traffic data such as synthetic network request data, syntheticpage load data, synthetic download outcomes, etc., may be generated,collected, processed and/or stored. In some operational scenarios, thesynthetic network traffic data may be collected using the same orsimilar mechanisms that are used to collect network traffic data inconnection with network requests and/or data downloads originated from(non-simulating) user devices and/or from user interactions.

Synthetic network traffic data may be generated, collected and/oranalyzed for dynamic performance evaluation purposes as well as dynamicperformance optimization purposes. In an example, before a new orrevised computer application is to be deployed in a specific environmentfor testing or production purposes, synthetic data may be collected byway of deploying or employing network request simulators 118 that issueor originate simulated network requests and/or corresponding datadownloads. In another example, after probability distributions ofnetwork optimization parameters are derived, some regular cases as wellas corner cases, fringe cases, outliers, etc., of the probabilitydistribution may be validated or complemented through collecting networktraffic data from simulated network requests and/or corresponding datadownloads to ensure relatively high quality and high confidence ofnetwork optimization policies generated by a system as described herein.In a further example, (candidate) optimized values of network or TCPparameters may be validated through synthetic traffic data collectedwith synthetic network requests and/or data downloads made with the(candidate) optimized values of the network or TCP parameters, beforethe (candidate) optimized values of the network or TCP parameters arepropagated to and implemented by user devices and/or network requestservers in a production environment, in a development environment, etc.A computer system as described herein may be a large system thatprovides computer applications/services to a large population of users(e.g., end users, developers, custom service professionals, etc.) inproduction and/or development environments or system instances. In someoperational scenarios, directly testing network performances of computerapplications and network optimization policies or strategies in theseenvironments may be relatively high risk and problematic. Techniques asdescribed herein can be used to avoid or reduce such testing in theproduction and development environments or system instances.

A network request simulator—which may simulate or emulate a user deviceand/or another network element—may be implemented using Linux systemswith curl, using a Linux kernel packet scheduler tc, using a trafficscenario simulator such as CatchPoint, and so forth. In someembodiments, a network request simulator as described herein may beimplemented or hosted with an application container such as a dockercontainer or a VM to simulate and collect traffic data corresponding tovarious network scenarios.

Synthetic traffic data as described herein may be used along withnon-synthetic traffic data (e.g., actual user generated traffic data,etc.) as a part of building blocks of statistical modeling. Sufficientstatistics derived at least in part from the synthetic traffic data maybe used to adjust or adapt user devices, application servers, etc., todifferent network conditions/settings, in place of or in addition toadjusting or adapting the user devices, application services based onmetadata information about access service network technology andlocation information of user devices. Synthetic traffic data may be usedgenerate training instances used to train a machine learningsystem/algorithm without a priori ground truth or labels. Synthetictraffic data can also be used to avoid or reduce testing new systems,new features, etc., directly on a production system or a multi-usermulti-group development system. Synthetic traffic data can be used toaid or enable modeling processes for identifying/validating performancecharacteristics of anew system, a new feature, etc. with a typical userpopulation (e.g., a user population of a large custom organizationhosted in a multitenant hosting system, etc.). Synthetic traffic datacan also be collected to validate a new system, a new feature, anewnetwork optimization strategy, etc., to see how the new system, the newfeature, the new network optimization strategy, etc., works in differentsettings of network conditions, in different values of a networkparameter, etc., in case statistical modeling with only past orhistorical data goes wrong. This can detect/avoid problems before theyactually occur.

7. Example Network Optimizer

FIG. 2 illustrates an example system that includes a network optimizer.The system or a module/component therein may be implemented in hardware,software, a combination of software and hardware, etc., with one or morecomputer devices. Some or all of the modules/components of the systemmay communicate over any combination of one or more of: the Internet;intranets, extranets, virtual private networks (VPNs), local areanetworks (LANs), wide area networks (WANs), wireless networks, wirelinenetworks, client-server, mobile networks, public networks, carrier-classnetworks, access networks, enterprise networks, proprietary networks, orthe like.

As illustrated in FIG. 2, a network optimizer 106 may include a(network) traffic data gatherer 202, a data aggregator 204, a heuristicsengine 206, a (data) model generator 208, a (sufficient) statisticsgenerator 222, a (data) tolerance adjustor 212, a machine learner 214, a(e.g., statistical, etc.) prediction generator 216, a performance dataset store 218, and a (network) policy propagator 220, in one embodiment.The network optimizer 106 may communicate data over one or more networks104 with other elements of system 100, such as user devices 102 and/ornetwork request simulators 118, one or more application servers 108,data centers 110, and one or more network traffic data stores 112.

A network traffic data gatherer 202 may read, from a network trafficdata store 112, one or more network data values associated with networkrequests (including but not limited to data requests) between networkrequest origination devices such as user devices 102 and/or networkrequest simulators 118 and data centers 110 through one or moreapplication servers 108. In one embodiment, a network data value may begathered by an agent 114 of a user device 102 and/or a network requestsimulator 118 or from an application server 108. The network trafficdata gatherer 202 may retrieve network traffic data stored in one ormore network traffic data stores 112 by the agent 114 or by theapplication server 108, in an embodiment.

A data aggregator 204 may retrieve raw network traffic data from thenetwork traffic data stores. The data aggregator 204 can aggregate theraw network traffic data into aggregated rows over a period of time(e.g., a month, a week, a day, an hour, etc.) for each time window/blockin the period of time. The aggregated rows are representative of the(e.g., mobile, user device originated, etc.) network traffic that isaimed for optimization. Each aggregated row becomes a data point withinformation on the “goodness” (e.g., “good”, “ok”, “bad”, other networkquality classifications/categories, etc.) for network requests and/ordata downloads represented in the aggregated row for the time window.

A sufficient statistics generator 222 may generate individual sufficientstatistics of network optimization parameters for each network qualitycategory identified for a time block or window as described herein.

Network quality mappings may be generated from different network trafficbuckets (e.g., of each network quality category, etc.) comprisingsubsets of the aggregated rows and stored as network performancedataset(s) in a network optimization data set store such as aperformance data set store 218 of FIG. 2, and may be used as data pointsto train a machine, for example in a “supervised” way.

A heuristics engine 206 may incorporate knowledge known toadministrators of the network optimizer 106. A heuristic is a technique,method, or set of rules designed for solving a problem more quickly whenclassic methods are too slow, or for finding an approximate solutionwhen classic methods fail to find any exact solution. Here, theheuristics engine 206 may incorporate knowledge known to the designersof supervised learning methods and techniques described herein toestimate network parameters.

A data model generator 208 may generate one or more data models toestimate optimized values for some or all network or TCP parameters asdescribed above. Given the possibility of network changes over time andthe deterministic nature of identifying sets of optimal values for thenetwork or TCP parameters using the aggregated rows or database records,the data model generator 208 may be used to identify an iterativeprocess for a supervised learning algorithm/method to train a machine toachieve desired outputs.

In an example, a particular network quality category may have a maximumthroughput of 50 MB/sec based on historical data. Thus, a transmissionrate, a particular network or TCP parameter, may be throttled to a rangeof 20 to 30 MB/sec to ensure faster transmission and minimize the riskof packet loss. In another example, a particular web page may have dataobjects of different criticalities or elasticities. Usability and properfunctioning of the particular web page may be ensured by adopting anorder of download in which relatively critical data objects of the webpage are downloaded first or by optimizing burst sizes and congestionhandling based on the respective elasticities of the data objects in theweb page. Example operations in connection with data object criticalitycan be found in U.S. patent application Ser. No. 16/775,834 (AttorneyDocket Number: 4475US; 80011-0060), with an application title of“CRITICAL PATH ESTIMATION FOR ACCELERATED AND OPTIMAL LOADING OF WEBPAGES” by Tejaswini Ganapathi, Kartikeya Chandrayana and SatishRaghunath, filed on Jan. 29, 2020, the entire contents of which arehereby incorporated by reference as if fully set forth herein. Exampleoperations in connection with data object elasticity can be found inU.S. patent application Ser. No. 16/775,847 (Attorney Docket Number:4474US; 80011-0059), with an application title of “NETWORK REQUEST ANDFILE TRANSFER PRIORITIZATION BASED ON TRAFFIC ELASTICITY” by TejaswiniGanapathi, Satish Raghunath and Kartikeya Chandrayana, filed on Jan. 29,2020, the entire contents of which are hereby incorporated by referenceas if fully set forth herein.

A machine learner 214 may implement (e.g., unsupervised, supervised,etc.) machine learning algorithm/model, for example using a networkperformance dataset comprising the network quality mappings describedabove—which may be stored in the performance data set store 218—astraining instances to train the machine learning algorithm/model. Themachine learner may be implemented with one or more computing devices.

A data tolerance adjustor 212 may ensure that an estimated (value for a)network or TCP parameter falls within a particular tolerance based on atype of the parameter. For discrete network or TCP parameter values,such as number of simultaneous network connections, the tolerance may bezero (0), for example. For continuous network or TCP parameter values,such as rate of transmission, the tolerance may be 10%, for example, incomparison with a black box optimization algorithm developed to retrieveor compute network or TCP parameters which maximized performance basedon calculation of network statistics. The black box optimizationalgorithm may operate with an objective function, which may be afunction of performance improvement in page load time, throughput,download complete time, network congestion, and other networkperformance characteristics/parameters. The optimization may beconstrained or dependent on thresholds for performance improvementmetrics and traffic share. The black box algorithm outputs a set ofvalues for the network or TCP parameters which optimizes the objectivefunction subject to the constraints. It operates on dataaggregated—which may or may not be the same as a time window for whichaggregated rows may be generated for training the machine for supervisedlearning—over some period of time (e.g., a few days, etc.) and has nomemory in the choice of statistics used to calculate this objectivefunction and is purely deterministic.

A statistical prediction generator 216 may be used to generatecalculations used in statistical prediction, including probabilitydistributions, Bayesian probability, moving averages, regressionanalysis, predictive modeling, and other statistical computations. Theperformance data set store 218 may be used to store training set datafor generated data models. The performance data set store 218 mayinclude a subset of data stored on the network traffic data store 112,in one embodiment.

A network optimization policy propagator 220 may deliver a networkoptimization policy to network request origination devices such as userdevices 102 and/or network request simulators 118 and/or applicationservers 108. A network optimization policy may be chosen based on theabove described techniques and may be propagated by configuring anetwork interface on a user device 102 through an agent 114 orconfiguring network traffic management on an application server 108through an accelerator 116, in an embodiment. In other embodiments, thenetwork optimization policy propagator 220 may send instructions to auser device 102 (and/or a network request simulator 118 in someoperational scenarios) or an application server 108 on how to implementthe chosen network optimization policy based on the estimated networkTCP parameter(s).

8. Estimating Network Quality Categories

FIG. 3A illustrates an example process flow for estimating networkquality categories, extracting relevant information for networkoptimization, and generating network performance dataset(s) in the formof network quality mappings to drive a network optimizer as describedherein, given network traffic data. In some embodiments, one or morecomputing devices or components may perform this process flow.

In some operational scenarios, the network quality categories areestimated repeatedly, for example every N hours, to make a system asdescribed herein or the network optimizer therein, adaptive to changesin network(s) (e.g., 104 of FIG. 1A or FIG. 1B, etc.). Additionally,optionally or alternatively, in some operational scenarios, the networkquality categories are estimated using bypass network requests operatingin a “bypass” mode without network optimization in order to remove biasfrom any network optimization algorithms on download outcomes andperceived network qualities.

Block 302 comprises sending, by one or more network request originationdevices such as user devices 102 and/or network request simulators 118,network requests for data or data objects of one or more web pages ofone or more computer applications to one or more application servers108.

Block 304 comprises, in response to receiving the network requests,measuring, by the application servers 108, network traffic data valuesfor the received network requests.

Block 306 comprises, in response to receiving the data or data objectsfrom the application servers 108, measuring, by the network requestorigination devices, raw network traffic data values for the receiveddata or data objects as requested by the network requests. Such rawnetwork traffic data values may include page load data, downloadoutcomes, etc.

Block 308 comprises gathering, by a network optimizer 106, networktraffic data comprising one or more network traffic data portions overone or more time blocks/windows. Each network traffic data portion inthe one or more network traffic data portions may be gathered orcollected for a respective time block/window in the one or more timeblocks/windows.

In some operational scenarios, a network traffic data portion over acorresponding time block/window is partitioned into one or moredifferent (traffic data) data segments corresponding to one or moredifferent combinations of computer applications (e.g., “xyz-app-1”,“xyz-app-2”, etc.) and geographic or time zones (e.g., “us-west-1”,“us-west-2”, “us-east-1”, “us-east-2”, etc.) in which network requestorigination devices operating with the computer applications reside.Each data segment in the one or more data segments corresponds to arespective (e.g., unique, distinct, etc.) combination of a specificcomputer application and a specific geographic zone in the one or morecombinations of computer applications and geographic zones.

Example computer applications as described herein may include, but arenot necessarily limited to only, any of: standard computer applicationsprovided by a multitenant hosting system to all customer organizationshosted in the multitenant hosting system; custom computer applicationsdeveloped by respective hosted organizations; computer applicationsinteracting with mobile applications and/or desktop applications;computer applications interacting with or providing web pages displayedwith web browsers; and so forth.

Example geographic or time zones (or locations) may include, but are notnecessarily limited to only, any of: Pacific Time Zone; Central TimeZone; Eastern Time Zone; regions of a continent; regions of a country;regions of a state or province; regions in which access service networkswith their service areas physically covering the network requestorigination devices are located; regions with or in which closestapplication servers (of a provider of the computer applications) thatprocess network requests in connection with the computer applicationsare deployed, and so forth.

The network optimizer 106 may filter the network traffic data (e.g.,filtering each network traffic data portion in some or all of the datasegments, etc.) to generate bypass network traffic data (e.g., generatea bypass network traffic data portion for each data segment in some orall of the data segments, etc.). In some operational scenarios, anynetwork traffic data portion of the network traffic data that wasaccelerated with network optimization policies as described herein maybe excluded or filtered out from the bypass network traffic data.

Bypass network traffic data provides a purest unbiaseddefinition/representation for quality of networks, as samples or datapoints with accelerated performance may skew assessment of actualquality of networks. Some or all network requests and/or data downloadrepresented in the bypass network traffic may not go through a proxyservice that implements network optimization strategies. These networkrequests and/or data downloads may be exchanged directly between networkrequest origination devices and application servers. Additionally,optionally or alternatively, some or all network requests and/or datadownload represented in the bypass network traffic may go through atransparent proxy or a proxy operating in a transparent mode that doesnot implement network optimization strategies. Thus, network qualitycategories can be found by clustering aggregated rows or databaserecords (representing measurement/sampling data points) in the bypasstraffic data or a portion (e.g., a data segment, a sub data segment,etc.) therein.

Additionally, optionally or alternatively, in some operationalscenarios, all of the network traffic data is considered or deemed asbypass network traffic data. For example, filtering to generate bypasstraffic data may be turned off to include some or all of an entirepopulation of network traffic including but not limited to acceleratedtraffic as bypass traffic. As a result, network quality categories maybe generated with automatic clustering of aggregated rows or databaserecords that include those of accelerated traffic.

A data segment comprising a bypass network traffic data portion asdescribed herein may be represented as aggregated rows or databaserecords. Each aggregated row or database record in the (bypass traffic)data segment may comprise (bypass traffic) data fields including but notlimited to any, some or all of: a traffic share of network request(s)and/or data download(s) represented in the aggregated row or databaserecord; one or more page load metrics for web page(s) comprising data ordata objects as requested by the network request(s); one or moredownload outcomes of the requested data or data objects; an (e.g.,individual, average, etc.) access round trip time (RTT) determined fromarrival times of data packets representing the network request(s) and/orthe data download(s) between network request origination device(s)making the network request(s) and closest (e.g., application, proxy,etc.) servers receiving the network requests(s); a source IP prefix(e.g., IPv4 prefix, IPv6 prefix, etc.) to which the network requestorigination device(s) belong; an autonomous system number or ASN of anautonomous system through which the network request originationdevice(s) access application servers and/or data centers and download orexchange data with the application servers and/or the data center; etc.

As used herein, an autonomous system (AS) may refer to a collection ofconnected Internet Protocol (IP) routing prefixes under the control ofone or more network operators on behalf of a single administrativeentity or domain that presents a common, clearly defined routing policyto the Internet. Instead of setting up a probably overbroad networkoptimization policy across all different autonomous system numbers,techniques as described herein can be used to generate individualcustomized network optimization strategies/policies for sub scopes orsub data segments that are at least partly distinguished by differentcombinations of computer applications, geographic or time zones ofapplication servers processing network requests, IP prefixes, accessRTTs, source IP prefixes, different ASNs, etc.

Block 310 comprises extracting, by the network optimizer 106, one ormore network performance features from each aggregated row or databaserecord in aggregated rows or database records of each data segmentcomprising a corresponding bypass traffic data portion over a timeblock/window. Using the one or more network performance features foreach such aggregated row or database record, the network optimizer 106generates or estimates one or more network quality categories throughunsupervised learning. The one or more network quality categoriescomprise one or more (e.g., mutually exclusive, etc.) subsetscollectively constituting the aggregated rows or database records of thedata segment. Each network quality category in the one or more networkquality categories corresponds to a respective subset in one or more(e.g., mutually exclusive, etc.) subsets of aggregated rows or databaserecords in the data segment.

The one or more network performance features may be represented in afeature vector comprising one or more vector components each of whichcorresponds to a respective network performance feature in the one ormore network performance features. The one or more network performancefeatures may be one or more of: page load performance metrices, downloadoutcomes, etc.

Block 312 comprises calculating respective sufficient statistics (e.g.,a marginal probability distribution, parameters defining a marginalprobability distribution, etc.) of each of network optimizationparameters (e.g., sustained maximum transmit rate, maximum allowedburst, maximum latency, etc.) for each network performance category inone or more network performance categories determined for each datasegment comprising a bypass traffic data portion over a timeblock/window. The network optimization parameters may be generated fromthe aggregated rows or database records using a generative model. Invarious embodiments, the respective sufficient statistics of each of thenetwork optimization parameters may be calculated or determined usingone or more of: maximum likelihood, best fitting of a type ofprobability distribution, neural networks, etc.

Block 314 comprises identifying, by the network optimizer 106, one ormore traffic data buckets (including but not limited to their respectivetraffic shares) among some or all aggregated rows or database records ineach network performance category in one or more network performancecategories determined for each data segment comprising a bypass trafficdata portion over a time block/window.

The one or more traffic data buckets may be identified or determined inthe aggregated rows or database records of each such network performancecategory by a combination of data fields (or values thereof) of theaggregated rows or database records including but not limited to some orall of: one or more access RTT ranges, one or more source IP prefix(es),one or more ASN(s), etc. Additionally, optionally or alternatively,other data fields (or other values thereof) in the aggregated rows ordatabase records may be used to identify or add granularity to trafficdata buckets as described herein. Thus, each traffic data bucket in anetwork quality category for a time block/window as described hereinrepresents a sub data segment of a data segment comprising the networktraffic data portion of the network quality category for the timeblock/window.

The network optimizer 106 generates one or more network quality mappingsfrom one or more traffic data buckets in each network performancecategory in one or more network performance categories determined foreach data segment comprising a bypass traffic data portion over a timeblock/window. The one or more network quality mappings collectivelyrepresent a mapping from the network performance category to the one ormore traffic data buckets in the network performance category.

For example, multiple network traffic buckets of respectivecombinations/values of data fields (source IP, access RTT, ASN) may beidentified or generated for a single network quality category/band(e.g., “good”, “ok”, “bad”, etc.) as described herein. The mapping fromthe network quality category/band to the multiple network trafficbuckets is one to many. The system may take this into account by storingthe one-to-many mapping, as generated by the process flow of FIG. 3A, ina network quality mapping data structure and use this one-to-manymapping to drive an intelligent adaptive network optimization engine.

As the network optimization engine can be trained for each dynamicallygenerated network quality category with multiple dynamically generatednetwork traffic buckets and/or network quality mappings, and as bothnetwork quality categories and network traffic buckets (andcorresponding network quality mappings as training instances) can bedetermined intrinsically and dynamically from the network traffic data,the network optimization engine as described herein can operate without(dependence on) receiving metadata about access service networktechnology (e.g., metadata identifying WiFi, 3/4/5G, etc.) and/orwithout (dependence on) receiving geographic locations (e.g., other thangeographic or time zones of application servers processing networkrequests, etc.) of network request origination devices. However, itshould be noted that, in some operational scenarios, the networkoptimization engine can incorporate information/metadata on accessservice network technology and geographic locations of network requestorigination devices, if such information/metadata is available. Thus,techniques as described herein can be used to widen application scopesof network or TCP optimization policies/strategies to be used in a widevariety of application scenarios including but not limited to thoseapplication scenarios involving optimizing browser and mobile webtraffic.

9. Driving Network Optimization

FIG. 3B illustrates an example process flow for driving a networkoptimization system with network quality mappings. These network qualitymappings are derived from each of some or all data segments in networktraffic data gathered for a time block/window.

Block 322 comprises receiving network traffic data 320 for a timeblock/window, applying unsupervised learning to identify network qualitycategories in the network traffic data 320, and generating networkquality mappings (e.g., 308 of FIG. 3A, etc.) for each network qualitycategory based on aggregated rows or database records representing datasegment(s) and sub data segments in a respective network traffic dataportion for the network quality category.

As previously mentioned, in some operational scenarios, a data segmentmay be identified by computer application (e.g., on a per computerapplication basis, on a per computer application type basis, etc.) andgeographic or time zone (e.g., on a per time zone basis, on a pergeographic zone basis, etc.) of application server(s) processing networkrequests in connection with computer applications. In some operationalscenarios, a sub data segment may be further identified in a datasegment by access RTT range (e.g., on a per RTT range basis, an RTTrange of 0-50 milliseconds, an RTT range of 50-150 milliseconds, etc.),source IP prefix (e.g., on a per source IP prefix basis, a source IPprefix as determined from data packets sent by network requestorigination devices, etc.), ASN (e.g., on a per ASN basis, an autonomoussystem to which the network request origination devices belong or aredesignated, etc.), and so forth.

The network quality categories may be identified as clusters throughunsupervised learning by applying automatic clustering to aggregatedrows or database records in a data segment or a sub data segment asdescribed herein. Sufficient statistics of network optimizationparameters may be generated for a cluster of each network qualitycategory using generative models.

Individual weights for individual clusters of respective network qualitycategories may be adjusted from initial weights (e.g., default weightvalues, initial weight values of one (1), etc.) using traffic shares asrepresented in the individual clusters. For example, these individualweights of the categories/clusters may be set to be proportional torespective the traffic shares of the categories/clusters. In someoperational scenarios, original weights (e.g., one (1), etc.) ofsub-categories or sub data segments in clusters or categories can beadjusted by multiplying with traffic shares of the sub-categories or subdata segments.

Similarly, individual weights for network traffic buckets orcorresponding network quality mappings generated for a given networkquality category or cluster may be adjusted from initial weights (e.g.,default weight values, initial weight values of one (1), etc.) usingtraffic shares as represented in the network traffic buckets orcorresponding network quality mappings. For example, these individualweights of the network traffic buckets or corresponding network qualitymappings may be set to be proportional to respective the traffic sharesof the buckets or mappings.

Block 324 comprises using the network quality mappings by the networkoptimization system as input to generate network optimization policies326. Machine learning (e.g., supervised machine learning, unsupervisedmachine learning, etc.) may be applied for each network quality categoryby the network optimization system to generate the network optimizationpolicies 326.

In some operational scenarios, each of some or all of the networkoptimization policies 326 may be generated by the network optimizationsystem on a per sub data segment basis, as illustrated in FIG. 3C. Anetwork optimization policy 326 such as illustrated in FIG. 3C may bepropagated to some or all of: one or more accelerators (e.g., 116 ofFIG. 1A, etc.), one or more application servers (e.g., 108 of FIG. 1A orFIG. 1B, etc.), one or more proxy servers, one or more network requestorigination devices (e.g., user device(s) 102 of FIG. 1A or FIG. 1B,network request simulator(s) 118 of FIG. 1A or FIG. 1B, etc.), one ormore agents (e.g., 102 of FIG. 1A, etc.), one or more network/serverelements involving in processing network requests and corresponding datadownloads, etc. The network optimization policy 326 may be implementedindividually or collectively by one or more of these recipient devicesin new network requests and/or new data downloads originated orprocessed by the recipient devices of the network optimization policy.

A network optimization policy may comprise a number of networkoptimization strategies to be adaptively applied by a recipient devicein real time or in near real time. As illustrated in FIG. 3C, a networkoptimization policy may provide one or more network optimizationstrategies respectively for one or more corresponding networkperformance categories such as “good”, “ok”, “bad”, etc.

An accelerator as described herein may implement relatively fine-grainnetwork optimization strategies adaptively. One network optimizationstrategy may not adapt to the nuances among some or all user devices.

In some operational scenarios, if a network quality category ofnetwork(s) (e.g., 104 of FIG. 1A or FIG. 1B, etc.) is predicted,determined and/or estimated to be “good” at a given time, networkoptimization parameters from a corresponding network optimization policyfor the “good” network quality category may be retrieved and used by anaccelerator as described herein to determine optimal values for networkor TCP parameters.

If the network quality category of the network(s) is predicted,determined and/or estimated to be “ok” at a given time, networkoptimization parameters from a corresponding network optimization policyfor the “ok” network quality category may be retrieved and used by theaccelerator to determine optimal values for network or TCP parameters.

If the network quality category of the network(s) is predicted,determined and/or estimated to be “bad” at a given time, networkoptimization parameters from a corresponding network optimization policyfor the “bad” network quality category may be retrieved and used by theaccelerator to determine optimal values for network or TCP parameters.

In some operational scenarios, the network optimization parameters fromthe network optimization strategy may serve as upper bounds, averages,lower bounds, etc., in determining the optimal values for the network orTCP parameters. The network optimization parameters may be used togenerate optimal values for network or TCP parameter to be integrated orimplemented with TCP stacks of network request origination devicesand/or network request processing elements, thereby performing trafficshaping on network connections, for example on a per network or TCPconnection basis.

For example, a network optimization parameter like maximum burst size(in bytes) may be used by a network optimization system as an inputcondition for initial congestion window computation as well assubsequent congestion handling such that, at any point in time, thesender in a TCP connection will not exceed the maximum burst size.

In contrast, a TCP connection implemented without network optimizationtechniques as described herein may not have this optimal informationrepresented by the maximum burst size starting from scratch for the TCPconnection and is likely to be repeatedly and frequently penalizedduring the time period of the TCP connection in scenarios in which aburst size, an initial congestion window, threshold(s) used incongestion handling, etc., are set too large or exceeds the maximumburst size.

In some operational scenarios, as illustrated in FIG. 3C, a weight valuemay be assigned to a network optimization strategy as described herein.The weight value may be set in dependence to a traffic share representedby a network quality category in a data segment or a sub data segment. Anetwork optimization policy may be applied in a way that takes intoaccount traffic shares of different network quality categories.

By way of illustration but not limitation, in response to determiningthat a traffic share of a specific network quality category ispredominant as compared with other network quality categories for thesame data segment or for the same sub data segment, one or morerecipient devices of the network optimization policy can implement aspecific network optimization strategy (or network optimizationparameters therein) associated with or for the specific network qualitycategory more frequently, or as a default network optimization strategywhen real time network quality cannot be (e.g., confidently, initially,beforehand, etc.) determined.

Some network optimization strategies may be too aggressive. Some userdevices may work better with less aggressive network optimizationparameters generated for the “ok” network quality category rather thanwith more aggressive network optimization parameters generated for the“good” network quality category.

A network optimization solution as described herein can be extended tooperational scenarios in which there are no or few accelerated networkrequests. Additionally, optionally or alternatively, the solution can beextended to operational scenarios in which some or all page loadperformance data, download outcomes (e.g., time to first byte, etc.),etc., are not collected. In these operational scenarios, otherperformance metrics/variables such as round trip times (RTTs),inter-packet gaps, and so forth, that are affected by and/or indicativeof network quality may be used—e.g., in lieu of those not collected, notavailable and/or not used—to perform clustering or generating networkquality catalogs as described herein. Page load performance, downloadoutcomes, etc., are expected to depend on RTTs such as access RTTs fromnetwork request origination devices to edges of application servicenetworks (e.g., of a multitenant hosting system, etc.).

Some or all techniques as described herein can be implemented and/orperformed in a manner that is completely data driven with no or minimalhuman input. Such techniques may include, but are not limited to only,deciding granularity of data segments or sub data segments on whichnetwork optimization is to be performed to generate correspondingnetwork optimization policies. Additionally, optionally oralternatively, some or all techniques as described herein can beimplemented and/or performed without requiring or receiving metadatainformation on access network technology, such as WiFi, 3/4/5G, LTE,etc, and/or without requiring or receiving geographic or time zoneinformation from (e.g., geolocational capability) of user devices suchas mobile phones.

A network optimization system as described herein can implement anautonomous, adaptive and intelligent network optimization solutionthrough unsupervised and/or supervised machine learning. The systemautomatically adapts to real-time or near-real-time network qualitythrough recalculating or re-generating network quality categoriesrepeatedly, for example every N units of time, thereby providing anautonomous, adaptive and intelligent end-to-end solution that is able tofunction with no or minimum intervention and/or metadata informationother than those already inherently available in network traffic data.The end-to-end solution can provide network optimizationpolicies/strategies to be implemented by user devices operated by endusers and/or by origin servers that provide web-based computerapplications/services to the user devices and/or other servers/nodes(e.g., edge nodes, cache servers, etc.) between the user devices and theorigin servers.

10. Example Embodiments

FIG. 4C illustrates an example process flow that may be implemented by acomputing system (or device) as described herein. In block 442, a systemas described herein collects network request data over a time window fora web application that communicates with user devices from differentaccess networks over a plurality of application servers located at aplurality of different geographic locations.

In block 444, the system filters the network request data to generate aplurality of bypass network traffic records for the time window. Eachbypass network traffic record in the plurality of bypass network trafficrecords comprises one or more download outcomes.

In block 446, the system generates (e.g., through unsupervised machinelearning, etc.) a plurality of network performance categories from theplurality of bypass network traffic records. Each network performancecategory in the plurality of network performance categories comprises arespective subset of bypass network traffic records in a plurality ofsubsets of bypass network traffic records. The plurality of subsets ofbypass network traffic records collectively aggregates to the pluralityof bypass network traffic records.

In block 448, the system calculates a plurality of sets of sufficientstatistics of network optimization parameters for the plurality ofnetwork performance categories. Each set of sufficient statistics of thenetwork optimization parameters is calculated based on a respectivesubset of bypass network traffic records in the plurality of subsets ofbypass network traffic records.

In block 450, the system causes the plurality of sets of sufficientstatistics of the network optimization parameters to be used to improvenetwork communication performance of one or more web applications.

In an embodiment, the one or more web applications are different fromthe web application.

In an embodiment, the one or more web applications include the webapplication.

In an embodiment, the data download performances of the one or more webapplications are determined with simulated network requests usingsampled values of the network optimization parameters generated from thesufficient statistics of the network optimization parameters.

In an embodiment, the network performance categories are generatedthrough automatic clustering one or more features extracted from theplurality of bypass network traffic records for the time window.

In an embodiment, the one or more features comprise one or more of: oneor more page load performance metrics, one or more download outcomes, orone or more access round trip times.

In an embodiment, the sufficient statistics are generated with one ormore generative models.

FIG. 4D illustrates an example process flow that may be implemented by acomputing system (or device) as described herein. In block 462, a systemas described herein receives a plurality of bypass network trafficrecords for a web application that communicates with user devices from aplurality of different access networks in a time window over a pluralityof application servers located at a plurality of different geographiclocations.

The plurality of bypass network traffic records is clustered into aplurality of network performance categories. Each network performancecategory in the plurality of network performance categories comprises arespective subset of bypass network traffic records in the plurality ofbypass network traffic records.

In block 464, the system calculates a plurality of sets of sufficientstatistics of one or more network optimization parameters for theplurality of network performance categories. Each set of sufficientstatistics of the one or more network optimization parameters iscalculated for a corresponding network performance category based on itsrespective subset of bypass network traffic records.

In block 466, the system partitions the respective subset of bypassnetwork traffic records for the corresponding network performancecategory into one or more network traffic buckets, thereby generating aplurality of network traffic buckets for the plurality of networkperformance categories.

In block 468, the system generates, from the plurality of sets ofsufficient statistics and the plurality of network traffic buckets, aplurality of network quality mappings.

In block 470, the system uses the plurality of network quality mappingsas training instances to train a machine learner for generating networkoptimization policies.

In block 472, the system causes the one or more network optimizationpolicies generated by the machine learner to be propagated to one ormore user devices to be implemented by the one or more user devices inmaking network requests to the web application.

In an embodiment, each network quality mapping in the plurality ofnetwork quality mappings is weighed by a respective traffic sharerepresented in a network traffic bucket used to generate the networkquality mapping.

In an embodiment, expected values of the network optimization parametersare generated from the sufficient statistics of the network optimizationparameters to be used as optimized values of the network optimizationparameters.

In an embodiment, the sufficient statistics of the network optimizationparameters are used to generate optimized values of the networkoptimization parameters, which may be other than expected values of thenetwork optimization parameters generated from the sufficient statisticsof the network optimization parameters.

In an embodiment, the plurality of network quality mappings is derivedfrom the network traffic data intrinsically with no dependence oninformation about (a) network access technologies used by the userdevices and (b) locations of the user devices.

In an embodiment, the plurality of bypass network traffic records forthe web application is free of network traffic records for acceleratednetwork requests and accelerated data downloads.

In an embodiment, the plurality of bypass network traffic records forthe web application further includes network traffic records foraccelerated network requests and accelerated data downloads.

In some embodiments, process flows involving operations, methods, etc.,as described herein can be performed through one or more computingdevices or units.

In an embodiment, an apparatus comprises a processor and is configuredto perform any of these operations, methods, process flows, etc.

In an embodiment, a non-transitory computer readable storage medium,storing software instructions, which when executed by one or moreprocessors cause performance of any of these operations, methods,process flows, etc.

In an embodiment, a computing device comprising one or more processorsand one or more storage media storing a set of instructions which, whenexecuted by the one or more processors, cause performance of any ofthese operations, methods, process flows, etc. Note that, althoughseparate embodiments are discussed herein, any combination ofembodiments and/or partial embodiments discussed herein may be combinedto form further embodiments.

11. Implementation Mechanisms—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 5 is a block diagram that illustrates a computersystem 500 upon which an embodiment of the invention may be implemented.Computer system 500 includes a bus 502 or other communication mechanismfor communicating information, and a hardware processor 504 coupled withbus 502 for processing information. Hardware processor 504 may be, forexample, a general purpose microprocessor.

Computer system 500 also includes a main memory 506, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 502for storing information and instructions to be executed by processor504. Main memory 506 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 504. Such instructions, when stored innon-transitory storage media accessible to processor 504, rendercomputer system 500 into a special-purpose machine that isdevice-specific to perform the operations specified in the instructions.

Computer system 500 further includes a read only memory (ROM) 508 orother static storage device coupled to bus 502 for storing staticinformation and instructions for processor 504. A storage device 510,such as a magnetic disk or optical disk, is provided and coupled to bus502 for storing information and instructions.

Computer system 500 may be coupled via bus 502 to a display 512, such asa liquid crystal display (LCD), for displaying information to a computeruser. An input device 514, including alphanumeric and other keys, iscoupled to bus 502 for communicating information and command selectionsto processor 504. Another type of user input device is cursor control516, such as a mouse, a trackball, or cursor direction keys forcommunicating direction information and command selections to processor504 and for controlling cursor movement on display 512. This inputdevice typically has two degrees of freedom in two axes, a first axis(e.g., x) and a second axis (e.g., y), that allows the device to specifypositions in a plane.

Computer system 500 may implement the techniques described herein usingdevice-specific hard-wired logic, one or more ASICs or FPGAs, firmwareand/or program logic which in combination with the computer systemcauses or programs computer system 500 to be a special-purpose machine.According to one embodiment, the techniques herein are performed bycomputer system 500 in response to processor 504 executing one or moresequences of one or more instructions contained in main memory 506. Suchinstructions may be read into main memory 506 from another storagemedium, such as storage device 510. Execution of the sequences ofinstructions contained in main memory 506 causes processor 504 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperation in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage device 510.Volatile media includes dynamic memory, such as main memory 506. Commonforms of storage media include, for example, a floppy disk, a flexibledisk, hard disk, solid state drive, magnetic tape, or any other magneticdata storage medium, a CD-ROM, any other optical data storage medium,any physical medium with patterns of holes, a RAM, a PROM, and EPROM, aFLASH-EPROM, NVRAM, any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 502. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 504 for execution. For example,the instructions may initially be carried on a magnetic disk or solidstate drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 500 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 502. Bus 502 carries the data tomain memory 506, from which processor 504 retrieves and executes theinstructions. The instructions received by main memory 506 mayoptionally be stored on storage device 510 either before or afterexecution by processor 504.

Computer system 500 also includes a communication interface 518 coupledto bus 502. Communication interface 518 provides a two-way datacommunication coupling to a network link 520 that is connected to alocal network 522. For example, communication interface 518 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 518 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 518sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 520 typically provides data communication through one ormore networks to other data devices. For example, network link 520 mayprovide a connection through local network 522 to a host computer 524 orto data equipment operated by an Internet Service Provider (ISP) 526.ISP 526 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 528. Local network 522 and Internet 528 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 520and through communication interface 518, which carry the digital data toand from computer system 500, are example forms of transmission media.

Computer system 500 can send messages and receive data, includingprogram code, through the network(s), network link 520 and communicationinterface 518. In the Internet example, a server 530 might transmit arequested code for an application program through Internet 528, ISP 526,local network 522 and communication interface 518.

The received code may be executed by processor 504 as it is received,and/or stored in storage device 510, or other non-volatile storage forlater execution.

12. Equivalents, Extensions, Alternatives and Miscellaneous

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. Thus, the sole and exclusive indicatorof what is the invention, and is intended by the applicants to be theinvention, is the set of claims that issue from this application, in thespecific form in which such claims issue, including any subsequentcorrection. Any definitions expressly set forth herein for termscontained in such claims shall govern the meaning of such terms as usedin the claims. Hence, no limitation, element, property, feature,advantage or attribute that is not expressly recited in a claim shouldlimit the scope of such claim in any way. The specification and drawingsare, accordingly, to be regarded in an illustrative rather than arestrictive sense.

What is claimed is:
 1. A computer-implemented method, comprising:collecting network request data over a time window for a web applicationthat communicates with user devices from different access networks atdifferent geographic locations; filtering the network request data togenerate a set of bypass network traffic records for the time window,wherein each bypass network traffic record in the set of bypass networktraffic records includes a measurement of a page data downloadperformance metric for user traffic represented in the bypass networktraffic record; dividing the set of bypass network traffic records intomultiple network performance categories each of which includes arespective subset of bypass network traffic records in the set of bypassnetwork traffic records; wherein the multiple network performancecategories are distinguished from one another based at least in part onmeasurements of the page data download performance metric; calculatingsets of sufficient statistics of network optimization parameters for themultiple network performance categories based on the subset of bypassnetwork traffic records; causing the sets of sufficient statistics ofthe network optimization parameters to be used to generate networkoptimization parameters to improve data download performances of the webapplication.
 2. The method as recited in claim 1, wherein at least aportion of the user traffic is generated with simulated networkrequests.
 3. The method as recited in claim 1, wherein the multiplenetwork performance categories are generated through automaticclustering one or more features extracted from the set of bypass networktraffic records for the time window.
 4. The method as recited in claim1, wherein the page data download performance metric represents one of:a web page load performance metric, a web page download outcome, or anaccess round trip time relating to web page downloading.
 5. The methodas recited in claim 1, wherein the sufficient statistics are generatedwith one or more generative models.
 6. The method as recited in claim 5,wherein the one or more generative models are trained with a trainingdataset.
 7. The method as recited in claim 5, wherein the one or moreprediction models implement supervised training.
 8. A non-transitorycomputer readable medium storing a program of instructions that isexecutable by a device to perform a method, the method comprising:collecting network request data over a time window for a web applicationthat communicates with user devices from different access networks atdifferent geographic locations; filtering the network request data togenerate a set of bypass network traffic records for the time window,wherein each bypass network traffic record in the set of bypass networktraffic records includes a measurement of a page data downloadperformance metric for user traffic represented in the bypass networktraffic record; dividing the set of bypass network traffic records intomultiple network performance categories each of which includes arespective subset of bypass network traffic records in the set of bypassnetwork traffic records; wherein the multiple network performancecategories are distinguished from one another based at least in part onmeasurements of the page data download performance metric; calculatingsets of sufficient statistics of network optimization parameters for themultiple network performance categories based on the subset of bypassnetwork traffic records; causing the sets of sufficient statistics ofthe network optimization parameters to be used to generate networkoptimization parameters to improve data download performances of the webapplication.
 9. The medium as recited in claim 8, wherein at least aportion of the user traffic is generated with simulated networkrequests.
 10. The medium as recited in claim 8, wherein the multiplenetwork performance categories are generated through automaticclustering one or more features extracted from the set of bypass networktraffic records for the time window.
 11. The medium as recited in claim8, wherein the page data download performance metric represents one of:a web page load performance metric, a web page download outcome, or anaccess round trip time relating to web page downloading.
 12. The mediumas recited in claim 8, wherein the sufficient statistics are generatedwith one or more generative models.
 13. The medium as recited in claim12, wherein the one or more generative models are trained with atraining dataset.
 14. The medium as recited in claim 12, wherein the oneor more prediction models implement supervised training.
 15. Anapparatus, comprising: one or more computing devices; a non-transitorycomputer readable medium storing a program of instructions that isexecutable by the one or more computing devices to perform a method, themethod comprising: collecting network request data over a time windowfor a web application that communicates with user devices from differentaccess networks at different geographic locations; filtering the networkrequest data to generate a set of bypass network traffic records for thetime window, wherein each bypass network traffic record in the set ofbypass network traffic records includes a measurement of a page datadownload performance metric for user traffic represented in the bypassnetwork traffic record; dividing the set of bypass network trafficrecords into multiple network performance categories each of whichincludes a respective subset of bypass network traffic records in theset of bypass network traffic records; wherein the multiple networkperformance categories are distinguished from one another based at leastin part on measurements of the page data download performance metric;calculating sets of sufficient statistics of network optimizationparameters for the multiple network performance categories based on thesubset of bypass network traffic records; causing the sets of sufficientstatistics of the network optimization parameters to be used to generatenetwork optimization parameters to improve data download performances ofthe web application.
 16. The apparatus as recited in claim 15, whereinat least a portion of the user traffic is generated with simulatednetwork requests.
 17. The apparatus as recited in claim 15, wherein themultiple network performance categories are generated through automaticclustering one or more features extracted from the set of bypass networktraffic records for the time window.
 18. The apparatus as recited inclaim 15, wherein the page data download performance metric representsone of: a web page load performance metric, a web page download outcome,or an access round trip time relating to web page downloading.
 19. Theapparatus as recited in claim 15, wherein the sufficient statistics aregenerated with one or more generative models.
 20. The apparatus asrecited in claim 19, wherein the one or more generative models aretrained with a training dataset.
 21. The apparatus as recited in claim19, wherein the one or more prediction models implement supervisedtraining.